In my Globe Business Analytics: Formula

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Standard Deviation Calculation

Introduction

In statistics, standard deviation is a measure of the amount of variation or dispersion in a set of data. It is often used as a measure of how spread out a distribution is, or how far the values in a set are from the average value. Standard deviation is a powerful tool that is widely used in statistical analysis, hypothesis testing, and quality control.

This article will provide a comprehensive overview of standard deviation, including its calculation, interpretation, and applications. We will also discuss some of the advantages and limitations of standard deviation, as well as some of the common misconceptions surrounding this statistical measure.

Quartiles Formula

Quartiles are a useful statistical tool in business statistics for dividing a dataset into four equal parts, each representing 25% of the data. They are often used in conjunction with other measures of central tendency and variability, such as the mean and standard deviation, to better understand the distribution of the data.

The quartiles of a dataset can be calculated using the following formula:

Q1 = L + (N/4 - F) * (U - L)/C

Q2 = L + (N/2 - F) * (U - L)/C

Q3 = L + (3N/4 - F) * (U - L)/C

Percentile Calculation Formula

Percentiles are a way to represent where a given value falls within a distribution of values. The percentile of a value is the percentage of the data that is equal to or below that value.

To calculate the percentile of a given value, you need to follow these steps:

Sort the data in ascending order.

Count the number of values that are less than or equal to the given value.

Divide the count from step 2 by the total number of values in the dataset.

Multiply the result from step 3 by 100 to get the percentile.

Sample Mean Formula

Sample Mean: What It Is and How to Calculate It

In statistics, the sample mean is a commonly used measure of central tendency that represents the average value of a sample of data. It is calculated by adding up all of the values in the sample and dividing by the number of values.

For example, let's say we have a sample of test scores for 10 students:

78, 85, 92, 76, 89, 80, 87, 84, 91, 83